A texture-based classification method for classifying built areas according to their density

Karathanassi, V; Iossifidis, C; Rokos, D

dc.contributor.author	Karathanassi, V	en
dc.contributor.author	Iossifidis, C	en
dc.contributor.author	Rokos, D	en
dc.date.accessioned	2014-03-01T01:15:27Z
dc.date.available	2014-03-01T01:15:27Z
dc.date.issued	2000	en
dc.identifier.issn	0143-1161	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13510
dc.subject.classification	Remote Sensing	en
dc.subject.classification	Imaging Science & Photographic Technology	en
dc.title	A texture-based classification method for classifying built areas according to their density	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	Quantitative information about building density can serve as a useful tool for urban applications, such as the study of urban land changes, illegal building development, urban expansion, etc. This paper presents a method that classifies built areas according to their density into three categories using SPOT panchromatic remote sensing images. It is based on the concept of statistical measurement of texture. Specifically, three algorithms, which employ occurrence frequency or co-ocurrence matrices concepts on binary data, were developed, tested over the broader Athens area in Attica and evaluated. The developed algorithms were equally effective when windows larger than 31 x 31 pixels were used. For such windows, the overall accuracy of the method ranges from 83.40 to 89.61%. These results are better than the 79.70% that was obtained using the maximum likelihood classifier. The kappa(hat) coefficient was also improved by about 0.16 units, while the accuracy of the classification for each class of built areas was improved by about 50-60%.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	INTERNATIONAL JOURNAL OF REMOTE SENSING	en
dc.identifier.isi	ISI:000087175100003	en
dc.identifier.volume	21	en
dc.identifier.issue	9	en
dc.identifier.spage	1807	en
dc.identifier.epage	1823	en